A RISK ANALYSIS OF THE MOLYBDENUM-99 SUPPLY CHAIN USING BAYESIAN - - PowerPoint PPT Presentation

A RISK ANALYSIS OF THE MOLYBDENUM-99 SUPPLY CHAIN USING BAYESIAN NETWORKS

2017 Mo-99 Topical Meeting Jeffrey Liang, D.Eng.

OVERVIEW

• Motivation
• Background and Problem Description
• Research Questions and Limitations
• Methodology
• Findings
• Conclusions and Future Recommendations

MOTIVATION

• Subject for Dissertation, focusing on Engineering Management
• National Research Universal (NRU) reactor ceased production of Molybdenum-99 (99Mo) in Oct 2016
• Represents 19% of global 99Mo production
• Only producer in North America
• Effects of the NRU shutdown on the 99Mo supply chain is the subject of debate
• National Academy of Sciences: “>50% likelihood of severe shortages”
• Nuclear Energy Agency: “supply chain capacity should be sufficient”
• Majority of the remaining reactors are over 45 years old

METHODOLOGY

• Bayesian Network
• Each risk or performance measure is represented as an event
• Captures the likelihood of a given chain of events occurring
• Allows for back-propagation to see what parent events caused an outcome
• Modeled Reactor to Processor section of the supply chain
• Once processed into Generators, 99Mo can be shipped anywhere by air
• Each reactor and processing facility was a node in the network
• Quantity of 99Mo produced or processed was the outcome of each node

WHAT IS A BAYESIAN NETWORK?

• Extension of Bayes’ Theorem, which represents the probability of a hypothesis occurring after considering the effect
• f evidence on past experience
• Provides a way to combine both evidence and subjective beliefs
• Particularly useful in situations where there is a high degree of uncertainty
• The network consists of nodes and arcs
• Each node represents variables; each arc denotes parent-child relationships
• Each node has a conditional probability table that lists each of the different combinations of values from parent nodes and

the probabilities of that outcome occurring

BAYESIAN EXAMPLE

• 20% Chance of Rain
• If it is not raining, the sprinklers

are set to turn on 40% of the time

• If it rains, there is a 1% chance the

rain sensor will fail and the sprinkler will still activate

• What is the probability the grass

will be wet at any given time?

NETWORK DIAGRAM

Reprinted from “Molybdenum-99 for Medical Imaging” (p. 53), by the National Academies of Sciences, Engineering, and Medicine, 2016, Washington, DC: The National Academies Press.

Mo-99 Supply Chain Bayesian Network Model

LIMITATIONS

• Could not access business-specific operating information
• Proprietary Information
• Reactor scheduling or production decisions
• Processor sourcing decisions
• Does not include actual vs planned operating data
• Impact on Model
• Used typical number of operating days to calculate probability of operating
• Model is focused on determining probability of final production levels
• Not a system dynamics or stock-and-flow model
• Does not illustrate how companies would choose where to ship

PROBABILITY TABLES

Reactor Normal Production Maximum Production Value Probability Value Probability NRU

23.29% 23.29% 4680 76.71% 4680 76.71%

Maria

45.21% 45.21% 1500 54.79% 1500 0.00% 2700 0.00% 2700 54.79%

HFR

27.12% 27.12% 4680 72.88% 4680 0.00% 5400 0.00% 5400 72.88%

BR-2

47.95% 47.95% 5200 52.05% 5200 0.00% 7800 0.00% 7800 52.05%

LVR-15

42.67% 42.67% 600 57.33% 0.00% 2400 0.00% 2400 57.33%

SAFARI-1

16.44% 16.44% 2500 83.56% 2500 0.00% 3000 0.00% 3000 83.56%

• Each reactor node’s probability table was based on:
• Normal operating level
• Maximum operating level
• Number of operating days per year
• Not meant to be an accurate model of actual production levels
• Illustrates the validity of using Bayesian Networks
• Quantify risk in the supply chain
• The data in the tables can be updated with more accurate data

Complete Bayesian Network

• Determines the probability of different levels
• f Mo-99 production in the supply chain
• Allows for “what-if” scenarios
• What if reactor X has unscheduled

downtime?

• Enter an outcome and find the root cause
• If a major shortage took place, what

node(s) were the likely root cause(s)?

SCENARIOS

• Prior to NRU Production Cessation
• Probability of shortages with normal and maximum production rates
• After NRU Production Cessation
• Probability of shortages with normal and maximum production rates
• Probability of shortages if another reactor goes offline
• Root Causes of a major or minor shortage

FINDINGS

• Supply chain can meet demand after

NRU shutdown, but reactor coordination will be critical

• Very difficult to handle additional

unscheduled outages

Normal Production Maximum Production Pre-NRU Cessation Post-NRU Cessation

ROOT CAUSE ANALYSIS

• HFR is largest source of risk
• Not largest producer
• Longest operating period
• SAFARI-1 has significant impact despite being

mid-level producer

• Single supplier to NTP
• Loss of entire NTP supply

Normal Production Maximum Production Major Shortage Minor Shortage

CONCLUSIONS

• Theoretically there is enough production capacity in other reactors to compensate for the loss of NRU, but there are

significant sources of risk:

• Processing facilities do not have the capacity to processing more targets
• Multiple processing facilities can only be supplied by one reactor
• Results are a middle ground between NASM and NEA assessments
• 24% chance of major shortage (NASM: >50%, NEA: no impact)
• Operating schedule is just as important as production capacity
• SAFARI-1 and NTP will guarantee a shortage if offline

CONTRIBUTIONS/FUTURE WORK

• Contributions
• Existing assessments focused on only maximum production scenarios
• Prior studies do not quantify the risk each node introduces
• Prior work did not quantify probability of shortages based on different reactor outages
• Future Work
• Extending the model
• Different production levels for reactors
• Incorporate actual scheduling/coordination
• Real-time decision making tool
• Geographic analysis of facility locations
• Where to build new facilities mitigate the most risk
• Which facilities are best suited for adding capacity

QUESTIONS?